Behavioral Analysis

Overview

Unlike traditional testing that validates functionality ("does it work?"), behavioral analysis evaluates predictability ("will it stay within expected boundaries?"). This is critical because AI agents are non-deterministic - they make autonomous decisions that can drift over time, even with identical code and prompts.

Agent Inspector's behavioral analysis establishes your agent's behavioral perimeter - the range of acceptable operational patterns. It combines consistency metrics, stability scores, and outlier detection to catch both immediate anomalies and slow behavioral drifts before they reach production. Without this analysis, you're deploying blind to how your agent will actually behave at scale.

Behavioral Clustering

After running multiple sessions, Agent Inspector automatically groups them into behavioral clusters based on tool usage patterns, execution flow, and resource consumption. This reveals distinct operational modes and helps you understand how your agent actually behaves in practice.

Behavioral clustering showing distinct operational patterns and session distribution

Understanding Clusters

Each cluster represents a distinct "mode" of operation. For example, an agent might have:

• Cluster 1 (70% of sessions): Simple queries answered directly without tools
• Cluster 2 (20% of sessions): Complex queries requiring multiple tool calls
• Cluster 3 (10% of sessions): Error recovery and retry patterns

What Clustering Reveals

Cluster Distribution Interpretation

How Clustering Works

Agent Inspector uses advanced clustering algorithms to efficiently group similar sessions at scale. Each session is converted into a behavioral signature based on:

• Tool call sequences and patterns
• Resource utilization (tokens, time)
• Response characteristics
• Error patterns and recovery
• Execution flow and decision points

Outlier Detection

Sessions that don't fit into any major cluster are flagged as outliers. Each outlier receives deterministic root cause analysis identifying exactly why it deviated from normal patterns.

Detailed outlier detection with root cause analysis for anomalous sessions

What Are Outliers?

Outliers are sessions with Jaccard similarity < 0.4 to any cluster centroid. They represent anomalous executions that deviate significantly from normal patterns.

Why Outlier Detection Exists

• Anomaly Detection: Identifies when your agent behaves unexpectedly
• Security: High outlier rates may indicate prompt injection or adversarial inputs
• Quality Control: Helps catch edge cases that need handling
• Capacity Planning: Outliers often use more resources (tokens, latency)

Common Outlier Indicators

• Edge cases you didn't anticipate during development
• Error conditions that need better handling
• Inputs that confuse the agent or trigger unexpected behavior
• Performance anomalies or resource spikes
• Unexpected tool call sequences or missing standard tools

Root Cause Analysis

For each outlier, Agent Inspector provides deterministic analysis showing:

Outlier Rate Interpretation

Outlier Rate	Assessment	Action Required
0-5%	Excellent	Very few anomalies (HIGH confidence)
6-10%	Good	Some edge cases (MEDIUM confidence)
11-19%	Acceptable	Notable anomalies (PASSES but monitor)
≥20%	CRITICAL	Too many unpredictable sessions (FAILS gate)

When Outlier Rate is High (≥20%)

Root Causes:

• Diverse input patterns: Users submitting widely varying requests
• Error-handling paths: Failures create different behavioral patterns
• Dynamic tool selection: Agent choosing unpredictable tool combinations
• Prompt injection attempts: Malicious users bypassing instructions
• Insufficient training data: Not enough examples to form stable clusters

Action Steps:

1. Analyze Outlier Root Causes: Look for common patterns in outlier sessions, identify which features diverge
2. Handle Common Patterns: Define standard tool sequences, add timeout guardrails, implement response length limits
3. Improve Input Validation: Add request classification, reject out-of-scope requests early, sanitize inputs
4. Expand Coverage: Run more sessions to form additional clusters, update prompts for edge cases
5. Investigate Security: Review outlier sessions for malicious patterns or prompt injection

Investigating Outliers

1. Review Root Cause: Read the specific reason provided by the analysis
2. Open Session Details: View the full conversation and execution timeline
3. Compare to Normal: Look at typical cluster sessions to see the difference
4. Reproduce Issue: Try to recreate the scenario with similar inputs
5. Fix or Document: Either fix the issue or document as expected edge case

Stability & Predictability Scores

Agent Inspector provides quantitative metrics that measure how consistent and predictable your agent's behavior is across sessions. These scores are critical for production readiness assessment.

Stability and predictability metrics showing behavioral consistency across sessions

Stability Score

Range: 0-100%
Calculation: (Largest Cluster Size) / (Total Completed Sessions)
Production Gate: Must be ≥80% to pass

Measures how consistently your AI agent behaves across sessions. High stability means most sessions follow the same pattern - critical for production safety. This score represents the percentage of sessions in the dominant behavioral pattern.

Why Stability Matters

• Production Safety: Ensures your agent won't suddenly behave differently in production
• Debugging: Helps identify when agent behavior has changed between versions
• Quality Assurance: Validates that prompts and configurations produce consistent results
• User Experience: Predictable agents create better, more reliable user experiences

Stability Score	Assessment	Meaning
90-100%	Highly Stable	Nearly all sessions follow the same pattern
80-89%	Stable (PASS)	Most sessions are consistent - production ready
60-79%	Moderately Stable	Significant variation exists (CRITICAL - fails gate)
<60%	Unstable	High variability, unpredictable (CRITICAL - fails gate)

When Stability Fails (Score < 80%)

Root Causes:

• Non-deterministic prompts: Prompts allow too much variation in responses
• Context-dependent behavior: Agent adapts too much to different inputs
• Tool usage inconsistency: Agent uses different tools for similar tasks
• Temperature too high: LLM settings allow excessive randomness

Action Steps:

1. Review System Prompt: Add explicit instructions for consistent behavior, define clear decision boundaries
2. Add Guardrails: Implement validation checks before agent actions, define allowable tool sequences
3. Adjust LLM Parameters: Lower temperature (try 0.3-0.5), use deterministic sampling
4. Analyze Outlier Sessions: Check sessions outside main cluster to identify triggers

Predictability Score

Range: 0-100%
Calculation: 1.0 - Outlier Rate
Threshold: ≥60% to pass (WARNING if below)

An estimate of the likelihood that future sessions will behave "normally" (not become outliers). High predictability means new sessions will likely fit existing patterns. If outlier rate is 15%, there's an 85% chance a new session will follow known patterns.

Why Predictability Matters

• Capacity Planning: Estimate resource needs for future sessions
• SLA Confidence: Understand likelihood of meeting performance targets
• Risk Assessment: Quantify uncertainty in agent deployments
• Monitoring: Set realistic alert thresholds

Interpretation

• 90-100%: Highly predictable - new sessions almost always fit patterns
• 70-89%: Moderately predictable - most new sessions will be consistent
• 60-69%: Somewhat predictable - noticeable variation expected (PASSES)
• <60%: Unpredictable - cannot reliably predict behavior (WARNING)

Business Impact:

• Low Predictability: Higher customer support costs due to unpredictable UX
• High Predictability: Lower operational risk, easier debugging, more reliable SLAs

Additional Behavioral Metrics

Uncertainty Level

Calculation: 1.0 - Stability Score
Threshold: ≤25% to PASS, >25% WARNING

The inverse of stability score, representing the portion of behavioral variance that remains unexplained by the dominant pattern. High uncertainty means significant unpredictable variation exists.

• 0-10%: Very low uncertainty - high confidence in predictions
• 11-20%: Low uncertainty - good confidence
• 21-25%: Moderate uncertainty - acceptable (PASSES)
• >25%: High uncertainty - significant unknown variance (WARNING)

Why it matters: Explicitly measures what we don't know. Higher uncertainty = higher risk in production, suggesting need for stricter guardrails and controls.

Cluster Formation

Threshold: ≥1 cluster to PASS, 0 clusters CRITICAL

Verification that at least one behavioral cluster successfully formed. Zero clusters means sessions are too diverse to group together, indicating chaotic behavior.

• 0 clusters: No patterns - chaotic, no consistency detected (CRITICAL)
• 1 cluster: Single pattern - uniform behavior across all sessions (ideal)
• 2-3 clusters: Multiple modes - agent has 2-3 distinct operating patterns
• 4+ clusters: High diversity - many behavioral modes (may be too variable)

Critical note: If you have 10+ sessions and 0 clusters form, this indicates a fundamental problem with your agent's design. Do not deploy to production until resolved.

Confidence Levels

The system calculates an overall confidence level based on cluster maturity, data volume, and outlier rate. This indicates how trustworthy the behavioral analysis is.

High Confidence 🟢

Requirements:

• Substantial data volume (≥30 sessions in single cluster, or ≥80 for 2 clusters, or ≥150 for 3+)
• Low outlier rate (≤5% with 200+ sessions)
• Mature, well-established clusters

Interpretation: Behavioral patterns are well-established and reliable. Metrics can be trusted for production decisions.

Use Cases:

• ✅ Safe to deploy agent updates
• ✅ Accurate capacity planning
• ✅ Reliable SLA commitments
• ✅ Trustworthy security posture

Medium Confidence 🟡

Requirements:

• Patterns emerging but need more data (≥15 sessions in single cluster, or ≥40 for 2 clusters, or ≥75 for 3+)
• Moderate outlier rate (6-10% with 200+ sessions)
• Growing clusters

Interpretation: Patterns are visible but not fully stabilized. Metrics provide directional guidance but shouldn't be over-interpreted.

Use Cases:

• ⚠️ Deploy with extra monitoring
• ⚠️ Use conservative resource estimates
• ⚠️ Set loose SLA targets

Low Confidence 🔴

Conditions:

• Insufficient data (<15 sessions)
• High outlier rate (>10% with 200+ sessions)
• Very small or unstable clusters

Interpretation: Not enough data to draw reliable conclusions, or behavior is genuinely unpredictable. Metrics should be considered preliminary.

Action Required:

• 🛑 Do not make production decisions yet
• 🛑 Gather more data before trusting metrics
• 🛑 Investigate high outlier rate causes
• 🛑 Consider redesigning agent if persistent

Why Behavioral Analysis Matters

Catch Production Issues Early

An agent that passes unit tests but exhibits unpredictable behavior will cause issues in production. Behavioral analysis identifies these problems during development when they're cheap to fix.

Validate Prompt Changes

When you modify prompts or tools, behavioral analysis shows whether you've improved consistency or introduced new unpredictable patterns. Compare before/after stability scores to measure impact.

Enable Confident Deployment

Security teams need quantitative evidence that an agent is production-ready. Behavioral stability scores provide that evidence with objective, measurable criteria.

Understand Operational Complexity

Multiple behavioral modes aren't always bad, but you need to know they exist. Clustering reveals whether your agent is simple (1 mode), multi-modal by design (2-3 modes), or chaotic (≥4 modes).

Best Practices

Run Sufficient Sessions

Behavioral analysis requires statistical significance. Minimum 20 sessions for basic clustering, but different confidence levels require different amounts of data:

• 20 sessions: Minimum for basic analysis
• 30-50 sessions: Ideal for reliable analysis with medium confidence
• 100+ sessions: Best for complex agents, provides high confidence
• 200+ sessions: Required for meaningful outlier rate evaluation

Use Diverse Test Inputs

Test with varied scenarios to capture the full range of agent behavior:

• Different user personas and intents
• Simple and complex queries
• Edge cases and error conditions
• Various input formats and styles
• Both typical and unusual scenarios

Baseline Before Changes

Establish a behavioral baseline before making changes to prompts or tools. This lets you objectively measure whether changes improved or degraded consistency.

Document Expected Modes

If your agent intentionally has multiple operational modes, document each one:

• What triggers each mode
• Expected behavior in each mode
• Acceptable range for cluster distribution
• Known edge cases and outliers

Track Trends Over Time

Run behavioral analysis regularly (e.g., with each PR or deployment) to catch regressions early. Monitor:

• Stability score trending up or down
• Outlier rate changes after modifications
• New clusters appearing unexpectedly
• Cluster size distribution shifts

Integration with Production Gates

Behavioral stability is one of the five production readiness gates. Your agent must achieve a stability score ≥80% to pass. This ensures only agents with proven consistency reach production.

The gate checks:

• Stability score meets threshold (≥80%)
• Outlier rate is acceptable (<20%)
• At least one cluster has formed (≥1)
• Behavioral patterns are documented and understood

Learn more about how behavioral stability integrates with other gates in the Risk Assessment documentation.